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Found 10 papers, 1 papers with code
Speed and Conversational Large Language Models: Not All Is About Tokens per Second
The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.
Can ChatGPT Learn to Count Letters?
Large language models (LLMs) struggle on simple tasks such as counting the number of occurrences of a letter in a word.
Understanding the Impact of Artificial Intelligence in Academic Writing: Metadata to the Rescue
This column advocates for including artificial intelligence (AI)-specific metadata on those academic papers that are written with the help of AI in an attempt to analyze the use of such tools for disseminating research.
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
This enables most inferences to run with the reduced precision model and only a small fraction requires the full model, so significantly reducing computation and energy while not affecting model performance.
Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters
In this paper, the dependability of Stable Diffusion is studied focusing on soft errors in the memory that stores the model parameters; specifically, errors are injected into some critical layers of the Transformer in different blocks of the image information creator, to evaluate their impact on model performance.
Concurrent Linguistic Error Detection (CLED) for Large Language Models
Since the proposed error detection mechanism only relies on the outputs of the model, then it can be used on LLMs in which there is no access to the internal nodes.
How many words does ChatGPT know? The answer is ChatWords
The benefits of ChatWords are illustrated with two case studies: evaluating the knowledge that ChatGPT has of the Spanish lexicon (taken from the official dictionary of the "Real Academia Espa\~nola") and of the words that appear in the Quixote, the well-known novel written by Miguel de Cervantes.
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems
In this paper, we introduce Concurrent Classifier Error Detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors.
Can Artificial Intelligence Reconstruct Ancient Mosaics?
A large number of ancient mosaics have not reached us because they have been destroyed by erosion, earthquakes, looting or even used as materials in newer construction.
Statistical modeling of adaptive neural networks explains coexistence of avalanches and oscillations in resting human brain
Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", indicating that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.
